





"""simple docstring"""





from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy

class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = 'EncodecFeatureExtractor'
      _SCREAMING_SNAKE_CASE							      = ('T5Tokenizer', 'T5TokenizerFast')





      def __init__(			self ,		lowercase ,		lowercase					)						->	int:
            super().__init__(lowercase ,		lowercase					)
            lowerCAmelCase    		=			self.feature_extractor
            lowerCAmelCase    		=			False





      def        _snake_case     (			self ,		lowercase=None ,		lowercase=None ,		lowercase=True					)						->	str:
            return self.tokenizer.get_decoder_prompt_ids(task=lowercase ,		language=lowercase ,		no_timestamps=lowercase					)





      def __call__(			self ,		*lowercase ,		**lowercase					)						->	Union[str, Any]:
            # For backward compatibility
            if self._in_target_context_manager:
                  return self.current_processor(*lowercase ,		**lowercase					)

            lowerCAmelCase    		=			kwargs.pop("""audio""" ,		lowercase					)
            lowerCAmelCase    		=			kwargs.pop("""sampling_rate""" ,		lowercase					)
            lowerCAmelCase    		=			kwargs.pop("""text""" ,		lowercase					)
            if len(lowercase					) > 0:
                  lowerCAmelCase    		=			args[0]
                  lowerCAmelCase    		=			args[1:]

            if audio is None and text is None:
                  raise ValueError("""You need to specify either an `audio` or `text` input to process."""					)

            if text is not None:
                  lowerCAmelCase    		=			self.tokenizer(lowercase ,		**lowercase					)

            if audio is not None:
                  lowerCAmelCase    		=			self.feature_extractor(lowercase ,		*lowercase ,		sampling_rate=lowercase ,		**lowercase					)

            if audio is None:
                  return inputs

            elif text is None:
                  return audio_inputs

            else:
                  lowerCAmelCase    		=			audio_inputs["""input_values"""]
                  if "padding_mask" in audio_inputs:
                        lowerCAmelCase    		=			audio_inputs["""padding_mask"""]
                  return inputs





      def        _snake_case     (			self ,		*lowercase ,		**lowercase					)						->	Tuple:
            lowerCAmelCase    		=			kwargs.pop("""audio""" ,		lowercase					)
            lowerCAmelCase    		=			kwargs.pop("""padding_mask""" ,		lowercase					)

            if len(lowercase					) > 0:
                  lowerCAmelCase    		=			args[0]
                  lowerCAmelCase    		=			args[1:]

            if audio_values is not None:
                  return self._decode_audio(lowercase ,		padding_mask=lowercase					)
            else:
                  return self.tokenizer.batch_decode(*lowercase ,		**lowercase					)





      def        _snake_case     (			self ,		*lowercase ,		**lowercase					)						->	Tuple:
            return self.tokenizer.decode(*lowercase ,		**lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase = None					)						->	List[np.ndarray]:
            lowerCAmelCase    		=			to_numpy(lowercase					)
            lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			audio_values.shape

            if padding_mask is None:
                  return list(lowercase					)

            lowerCAmelCase    		=			to_numpy(lowercase					)

            # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
            # token (so that the generated audio values are **not** treated as padded tokens)
            lowerCAmelCase    		=			seq_len - padding_mask.shape[-1]
            lowerCAmelCase    		=			1 - self.feature_extractor.padding_value
            lowerCAmelCase    		=			np.pad(lowercase ,		((0, 0), (0, difference)) ,		"""constant""" ,		constant_values=lowercase					)

            lowerCAmelCase    		=			audio_values.tolist()
            for i in range(lowercase					):
                  lowerCAmelCase    		=			np.asarray(audio_values[i]					)[
                      padding_mask[i][None, :] != self.feature_extractor.padding_value
                  ]
                  lowerCAmelCase    		=			sliced_audio.reshape(lowercase ,		-1					)

            return audio_values













"""simple docstring"""





import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
       import onnxruntime as ort


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =  {
    "tensor(bool)": np.bool_,
    "tensor(int8)": np.inta,
    "tensor(uint8)": np.uinta,
    "tensor(int16)": np.intaa,
    "tensor(uint16)": np.uintaa,
    "tensor(int32)": np.intaa,
    "tensor(uint32)": np.uintaa,
    "tensor(int64)": np.intaa,
    "tensor(uint64)": np.uintaa,
    "tensor(float16)": np.floataa,
    "tensor(float)": np.floataa,
    "tensor(double)": np.floataa,
}

class 						lowercase   :





      def __init__(			self ,		lowercase=None ,		**lowercase					)						->	int:
            logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future."""					)
            lowerCAmelCase    		=			model
            lowerCAmelCase    		=			kwargs.get("""model_save_dir""" ,		lowercase					)
            lowerCAmelCase    		=			kwargs.get("""latest_model_name""" ,		lowercase					)





      def __call__(			self ,		**lowercase					)						->	List[Any]:
            lowerCAmelCase    		=			{k: np.array(lowercase					) for k, v in kwargs.items()}
            return self.model.run(lowercase ,		lowercase					)





      @staticmethod
      def        _snake_case     (			lowercase ,		lowercase=None ,		lowercase=None					)						->	Optional[Any]:
            if provider is None:
                  logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""					)
                  lowerCAmelCase    		=			"""CPUExecutionProvider"""

            return ort.InferenceSession(lowercase ,		providers=[provider] ,		sess_options=lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase = None ,		**lowercase					)						->	str:
            lowerCAmelCase    		=			file_name if file_name is not None else ONNX_WEIGHTS_NAME

            lowerCAmelCase    		=			self.model_save_dir.joinpath(self.latest_model_name					)
            lowerCAmelCase    		=			Path(lowercase					).joinpath(lowercase					)
            try:
                  shutil.copyfile(lowercase ,		lowercase					)
            except shutil.SameFileError:
                  pass

            # copy external weights (for models >2GB)
            lowerCAmelCase    		=			self.model_save_dir.joinpath(lowercase					)
            if src_path.exists():
                  lowerCAmelCase    		=			Path(lowercase					).joinpath(lowercase					)
                  try:
                        shutil.copyfile(lowercase ,		lowercase					)
                  except shutil.SameFileError:
                        pass





      def        _snake_case     (			self ,		lowercase ,		**lowercase ,		)						->	str:
            if os.path.isfile(lowercase					):
                  logger.error(f'Provided path ({save_directory}) should be a directory, not a file'					)
                  return

            os.makedirs(lowercase ,		exist_ok=lowercase					)

            # saving model weights/files
            self._save_pretrained(lowercase ,		**lowercase					)





      @classmethod
      def        _snake_case     (			cls ,		lowercase ,		lowercase = None ,		lowercase = None ,		lowercase = False ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		**lowercase ,		)						->	Union[str, Any]:
            lowerCAmelCase    		=			file_name if file_name is not None else ONNX_WEIGHTS_NAME
            # load model from local directory
            if os.path.isdir(lowercase					):
                  lowerCAmelCase    		=			OnnxRuntimeModel.load_model(
                      os.path.join(lowercase ,		lowercase					) ,		provider=lowercase ,		sess_options=lowercase					)
                  lowerCAmelCase    		=			Path(lowercase					)
            # load model from hub
            else:
                  # download model
                  lowerCAmelCase    		=			hf_hub_download(
                      repo_id=lowercase ,		filename=lowercase ,		use_auth_token=lowercase ,		revision=lowercase ,		cache_dir=lowercase ,		force_download=lowercase ,		)
                  lowerCAmelCase    		=			Path(lowercase					).parent
                  lowerCAmelCase    		=			Path(lowercase					).name
                  lowerCAmelCase    		=			OnnxRuntimeModel.load_model(lowercase ,		provider=lowercase ,		sess_options=lowercase					)
            return cls(model=lowercase ,		**lowercase					)





      @classmethod
      def        _snake_case     (			cls ,		lowercase ,		lowercase = True ,		lowercase = None ,		lowercase = None ,		**lowercase ,		)						->	List[str]:
            lowerCAmelCase    		=			None
            if len(str(lowercase					).split("""@"""					)					) == 2:
                  lowerCAmelCase		,						lowerCAmelCase    		=			model_id.split("""@"""					)

            return cls._from_pretrained(
                model_id=lowercase ,		revision=lowercase ,		cache_dir=lowercase ,		force_download=lowercase ,		use_auth_token=lowercase ,		**lowercase ,		)






